import torch
from torch import nn
from d2l import torch as d2l  

import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)  

num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = nn.Parameter(torch.randn(
                            num_inputs, num_hiddens, requires_grad=True) * 0.01)
b1 = nn.Parameter(torch.zeros(num_hiddens, requires_grad=True))
W2 = nn.Parameter(torch.randn(
                            num_hiddens, num_outputs, requires_grad=True) * 0.01)
b2 = nn.Parameter(torch.zeros(num_outputs, requires_grad=True))
params = [W1, b1, W2, b2]  

def relu(X):
    """
    ReLU函数被定义为该元素与0的最大值。  
    通俗地说，ReLU函数通过将相应地活性值设为0，仅保留正元素并丢弃所有负元素。
    """
    a = torch.zeros_like(X)
    return torch.max(X, a)  

def net(X):
    X = X.reshape((-1, num_inputs))
    H = relu(X@W1 + b1) # 这里“@”代表矩阵乘法
    return (H@W2 + b2)  

loss = nn.CrossEntropyLoss(reduction='none')  

num_epochs, lr = 10, 0.1
updater = torch.optim.SGD(params, lr=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)